Towards the Design of Robust Trust and Reputation Systems

Abstract

In reputation systems for multiagent-based e-marketplaces, buying agents model the reputation of selling agents based on ratings shared by other buyers (called advisors). With the existence of unfair rating attacks from dishonest advisors, the effectiveness of reputation systems thus heavily relies on whether buyers can accurately determine which advisors to include in trust networks and their trustworthiness. In this paper, we propose two approaches to deal with unfair rating attacks. The first method is to combine the advantages of different categorical trust models. Secondly, we propose a novel multiagent evolutionary trust model (MET) where each buyer constructs its trust network (information about which advisors should be include in the network and their trustworthiness) by the evolutionary model. Experimental results demonstrate the proposed algorithms are more robust than the state-of-the-art trust models against various unfair rating attacks.